Identification and analysis based on genetic algorithm for proton exchange membrane fuel cell stack

The temperature of proton exchange membrane fuel cell stack and the stoichiometric oxygen in cathode have relationship with the performance and life span of fuel cells closely. The thermal coefficients were taken as important factors affecting the temperature distribution of fuel cells and components. According to the experimental analysis, when the stoichiometric oxygen in cathode is greater than or equal to 1.8, the stack voltage loss is the least. A novel genetic algorithm was developed to identify and optimize the variables in dynamic thermal model of proton exchange membrane fuel cell stack, making the outputs of temperature model approximate to the actual temperature, and ensuring that the maximal error is less than 1℃. At the same time, the optimum region of stoichiometric oxygen is obtained, which is in the range of 1.8 -2.2 and accords with the experimental analysis results. The simulation and experimental results show the effectiveness of the proposed algorithm.